Our main objective is to devellop motion planners specifically tailored to control
humanoid motions. We are looking into combined solutions using techniques such as:
Probabilistic Motion Planning, interpolation of motion captured data,
and the biologically inspired notion of motor primitives.

We focus on the collision-free arm motion generation for manipulation purposes in complex
and cluttered environments. Motions must be computed at acceptable
speeds for interactive applications with humanoids. We are also interested on planning
motions for mobile manipulators.

Decoupled Motion Planning Test and comparison of decoupled approaches to
arm motion planning, against planning
in the composite configuration space of the two arms (>=14 dimensions).

Decoupled Motion Optimization Decoupled arm motion optimization for motions computed
in the composite configuration space of the two arms.

Primitive Based Planning Planners able to switch between different configuration spaces,
for multi-state or multi-primitive motion generation. We are interested in planning
sequential and concurrent primitives.

Use of Motion Capture Data To reduce the dimensionality of the search space, and
to encode motion primitives generating more human-like motions. Several approaches
based on high dimensional interpolation and Inverse Kinematics are being investigated.

The following image exemplifies some recent results on maintaining dynamic roadmaps.
The green and red edges are the valid portion of the roadmap, while the gray edges are
the invalidated portions, according to the current position of the dynamic obstacles.
A cell decomposition of the workspace is used to efficiently update roadmap edges.

In certain situations, dynamic roadmaps can significatly reduce the time required to
plan motions in changing environments, when comparing to on-line planners.
We evaluated the method in several randomly generated scenarios.
The following image shows one of them.

Planning the Sequencing of Motion Primitives

We are able to plan biped walking motion using sampling-based search to find
valid connections between parameterized motion primitives. The next image shows one
motion obtained. We are currently investigating the use of concurrent primitives for
humanoid reaching, including as well locomotion control for mobile manipulators.

M. Kallmann, R. Bargmann and M. Mataric´.
"Planning the Sequencing of Movement Primitives".
Proceedings of the International Conference on Simulation of Adaptive Behavior (SAB),
Los Angeles, CA, July 13-17, 2004.

M. Kallmann and M. Mataric´.
"Motion Planning Using Dynamic Roadmaps".
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),
New Orleans, Louisiana, April 26 - May 1, 2004.

This work is supported by the DARPA
MARS 2020 Program project
"Acquisition of Autonomous Behaviors by Robotic Assistants", via
the NASA subcontract grant NAG9-1444
"Skill Learning by Primitives-Based Demonstration & Imitation".